Overview

Dataset statistics

Number of variables17
Number of observations1000
Missing cells8666
Missing cells (%)51.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.9 KiB
Average record size in memory136.1 B

Variable types

Numeric17

Alerts

ForestDensityL is highly correlated with ForestDensityM and 5 other fieldsHigh correlation
ForestDensityM is highly correlated with ForestDensityL and 4 other fieldsHigh correlation
ForestDensityS is highly correlated with ForestDensityL and 1 other fieldsHigh correlation
NoisePollutionRailwayL is highly correlated with NoisePollutionRailwayM and 3 other fieldsHigh correlation
NoisePollutionRailwayM is highly correlated with NoisePollutionRailwayL and 3 other fieldsHigh correlation
NoisePollutionRailwayS is highly correlated with NoisePollutionRailwayL and 1 other fieldsHigh correlation
NoisePollutionRoadL is highly correlated with Floor and 8 other fieldsHigh correlation
NoisePollutionRoadM is highly correlated with ForestDensityL and 7 other fieldsHigh correlation
NoisePollutionRoadS is highly correlated with NoisePollutionRoadL and 3 other fieldsHigh correlation
PopulationDensityL is highly correlated with ForestDensityL and 4 other fieldsHigh correlation
PopulationDensityM is highly correlated with ForestDensityL and 5 other fieldsHigh correlation
living_area is highly correlated with Plot_area and 1 other fieldsHigh correlation
rooms_combined is highly correlated with living_areaHigh correlation
Floor is highly correlated with NoisePollutionRoadLHigh correlation
Plot_area is highly correlated with living_areaHigh correlation
0 has 506 (50.6%) missing values Missing
Floor has 818 (81.8%) missing values Missing
ForestDensityL has 494 (49.4%) missing values Missing
ForestDensityM has 494 (49.4%) missing values Missing
ForestDensityS has 494 (49.4%) missing values Missing
NoisePollutionRailwayL has 494 (49.4%) missing values Missing
NoisePollutionRailwayM has 494 (49.4%) missing values Missing
NoisePollutionRailwayS has 494 (49.4%) missing values Missing
NoisePollutionRoadL has 494 (49.4%) missing values Missing
NoisePollutionRoadM has 494 (49.4%) missing values Missing
NoisePollutionRoadS has 494 (49.4%) missing values Missing
Plot_area has 883 (88.3%) missing values Missing
PopulationDensityL has 494 (49.4%) missing values Missing
PopulationDensityM has 494 (49.4%) missing values Missing
living_area has 517 (51.7%) missing values Missing
rooms_combined has 508 (50.8%) missing values Missing
Floor has 34 (3.4%) zeros Zeros
ForestDensityL has 33 (3.3%) zeros Zeros
ForestDensityM has 126 (12.6%) zeros Zeros
ForestDensityS has 308 (30.8%) zeros Zeros
NoisePollutionRailwayL has 321 (32.1%) zeros Zeros
NoisePollutionRailwayM has 373 (37.3%) zeros Zeros
NoisePollutionRailwayS has 437 (43.7%) zeros Zeros

Reproduction

Analysis started2023-01-16 15:50:04.055067
Analysis finished2023-01-16 15:50:28.406568
Duration24.35 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct983
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11145.117
Minimum3
Maximum21984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:28.456065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile995.55
Q15767.75
median10869
Q316701.75
95-th percentile21018.35
Maximum21984
Range21981
Interquartile range (IQR)10934

Descriptive statistics

Standard deviation6435.110413
Coefficient of variation (CV)0.5773928091
Kurtosis-1.189647511
Mean11145.117
Median Absolute Deviation (MAD)5479.5
Skewness0.01199401887
Sum11145117
Variance41410646.03
MonotonicityNot monotonic
2023-01-16T16:50:28.528567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94762
 
0.2%
204202
 
0.2%
55642
 
0.2%
208402
 
0.2%
77562
 
0.2%
12882
 
0.2%
59202
 
0.2%
23672
 
0.2%
198892
 
0.2%
71382
 
0.2%
Other values (973)980
98.0%
ValueCountFrequency (%)
31
0.1%
81
0.1%
621
0.1%
631
0.1%
2161
0.1%
2261
0.1%
2311
0.1%
2321
0.1%
2451
0.1%
3111
0.1%
ValueCountFrequency (%)
219841
0.1%
219301
0.1%
219091
0.1%
219071
0.1%
218991
0.1%
218971
0.1%
218831
0.1%
218801
0.1%
218501
0.1%
218451
0.1%

0
Real number (ℝ≥0)

MISSING

Distinct293
Distinct (%)59.3%
Missing506
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean1301541.154
Minimum13480
Maximum18500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:28.604065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum13480
5-th percentile276500
Q1544250
median856500
Q31390000
95-th percentile3974750
Maximum18500000
Range18486520
Interquartile range (IQR)845750

Descriptive statistics

Standard deviation1599910.921
Coefficient of variation (CV)1.229243437
Kurtosis37.44928437
Mean1301541.154
Median Absolute Deviation (MAD)388000
Skewness5.021103783
Sum642961330
Variance2.559714956 × 1012
MonotonicityNot monotonic
2023-01-16T16:50:28.680566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69000010
 
1.0%
7200008
 
0.8%
7900007
 
0.7%
4950006
 
0.6%
10500006
 
0.6%
9900006
 
0.6%
12500006
 
0.6%
12900005
 
0.5%
8900005
 
0.5%
5400005
 
0.5%
Other values (283)430
43.0%
(Missing)506
50.6%
ValueCountFrequency (%)
134801
0.1%
1000001
0.1%
1100002
0.2%
1280001
0.1%
1350001
0.1%
1450001
0.1%
1490001
0.1%
1600001
0.1%
1650001
0.1%
1750001
0.1%
ValueCountFrequency (%)
185000001
 
0.1%
129000001
 
0.1%
99000002
0.2%
94900001
 
0.1%
80000001
 
0.1%
70000001
 
0.1%
65000001
 
0.1%
59000004
0.4%
55000001
 
0.1%
54500001
 
0.1%

Floor
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)4.9%
Missing818
Missing (%)81.8%
Infinite0
Infinite (%)0.0%
Mean7.214285714
Minimum0
Maximum999
Zeros34
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:28.749065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4.95
Maximum999
Range999
Interquartile range (IQR)1

Descriptive statistics

Standard deviation73.93616191
Coefficient of variation (CV)10.2485769
Kurtosis181.8604159
Mean7.214285714
Median Absolute Deviation (MAD)1
Skewness13.48302891
Sum1313
Variance5466.556038
MonotonicityNot monotonic
2023-01-16T16:50:28.800067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
154
 
5.4%
252
 
5.2%
034
 
3.4%
324
 
2.4%
48
 
0.8%
56
 
0.6%
72
 
0.2%
9991
 
0.1%
81
 
0.1%
(Missing)818
81.8%
ValueCountFrequency (%)
034
3.4%
154
5.4%
252
5.2%
324
2.4%
48
 
0.8%
56
 
0.6%
72
 
0.2%
81
 
0.1%
9991
 
0.1%
ValueCountFrequency (%)
9991
 
0.1%
81
 
0.1%
72
 
0.2%
56
 
0.6%
48
 
0.8%
324
2.4%
252
5.2%
154
5.4%
034
3.4%

ForestDensityL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct385
Distinct (%)76.1%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.20616829
Minimum0
Maximum0.7818335524
Zeros33
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:28.866065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02856369354
median0.1332614169
Q30.3345537874
95-th percentile0.624348801
Maximum0.7818335524
Range0.7818335524
Interquartile range (IQR)0.3059900938

Descriptive statistics

Standard deviation0.2129651927
Coefficient of variation (CV)1.032967741
Kurtosis-0.1676070279
Mean0.20616829
Median Absolute Deviation (MAD)0.1187016948
Skewness0.9996778791
Sum104.3211547
Variance0.04535417329
MonotonicityNot monotonic
2023-01-16T16:50:28.941065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
3.3%
0.72873754937
 
0.7%
0.0014637652845
 
0.5%
0.52930403924
 
0.4%
0.62017034844
 
0.4%
0.26260102863
 
0.3%
0.031598674753
 
0.3%
0.028553053063
 
0.3%
0.022002444453
 
0.3%
0.36248603873
 
0.3%
Other values (375)438
43.8%
(Missing)494
49.4%
ValueCountFrequency (%)
033
3.3%
0.00058042898961
 
0.1%
0.00085192069461
 
0.1%
0.0014398667062
 
0.2%
0.0014637652845
 
0.5%
0.0014804210261
 
0.1%
0.0015267406441
 
0.1%
0.0015984341951
 
0.1%
0.00167380811
 
0.1%
0.0016759447251
 
0.1%
ValueCountFrequency (%)
0.78183355242
 
0.2%
0.77034037072
 
0.2%
0.735286621
 
0.1%
0.73179172221
 
0.1%
0.7291991321
 
0.1%
0.72873754937
0.7%
0.69766557632
 
0.2%
0.69617942941
 
0.1%
0.65958660142
 
0.2%
0.65626007231
 
0.1%

ForestDensityM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct309
Distinct (%)61.1%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.1395802141
Minimum0
Maximum0.7873727282
Zeros126
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.103565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.628899293 × 10-6
median0.03940907754
Q30.2394775828
95-th percentile0.5315413164
Maximum0.7873727282
Range0.7873727282
Interquartile range (IQR)0.2394739539

Descriptive statistics

Standard deviation0.1902572063
Coefficient of variation (CV)1.363067162
Kurtosis0.7582242843
Mean0.1395802141
Median Absolute Deviation (MAD)0.03940907754
Skewness1.367035369
Sum70.62758832
Variance0.03619780453
MonotonicityNot monotonic
2023-01-16T16:50:29.182064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0126
 
12.6%
0.63768549017
 
0.7%
0.36400168964
 
0.4%
0.35279672964
 
0.4%
0.33374559873
 
0.3%
0.00021600585293
 
0.3%
0.0060215736853
 
0.3%
0.38713553143
 
0.3%
0.087789710873
 
0.3%
8.920410497 × 10-63
 
0.3%
Other values (299)347
34.7%
(Missing)494
49.4%
ValueCountFrequency (%)
0126
12.6%
3.602476365 × 10-61
 
0.1%
3.708168076 × 10-62
 
0.2%
7.582025223 × 10-61
 
0.1%
8.920410497 × 10-63
 
0.3%
1.28428226 × 10-51
 
0.1%
1.328139803 × 10-51
 
0.1%
3.541161623 × 10-52
 
0.2%
5.282093802 × 10-51
 
0.1%
0.00014196474451
 
0.1%
ValueCountFrequency (%)
0.78737272822
 
0.2%
0.70651041822
 
0.2%
0.6843039942
 
0.2%
0.66144409241
 
0.1%
0.6594236341
 
0.1%
0.63768549017
0.7%
0.62122074481
 
0.1%
0.6103633011
 
0.1%
0.59964038991
 
0.1%
0.5960642671
 
0.1%

ForestDensityS
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct149
Distinct (%)29.4%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.08885116357
Minimum0
Maximum0.7699277131
Zeros308
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.269066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.07102366901
95-th percentile0.4956389601
Maximum0.7699277131
Range0.7699277131
Interquartile range (IQR)0.07102366901

Descriptive statistics

Standard deviation0.1707718521
Coefficient of variation (CV)1.921999051
Kurtosis2.870959633
Mean0.08885116357
Median Absolute Deviation (MAD)0
Skewness1.994489453
Sum44.95868877
Variance0.02916302547
MonotonicityNot monotonic
2023-01-16T16:50:29.344065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0308
30.8%
0.489897457519
 
1.9%
0.55598579757
 
0.7%
0.25030221744
 
0.4%
0.000966776934
 
0.4%
0.014151377063
 
0.3%
0.1518629733
 
0.3%
0.27964968563
 
0.3%
0.14659241722
 
0.2%
0.062574143122
 
0.2%
Other values (139)151
 
15.1%
(Missing)494
49.4%
ValueCountFrequency (%)
0308
30.8%
0.00028724077621
 
0.1%
0.00044687451441
 
0.1%
0.00047719916231
 
0.1%
0.000966776934
 
0.4%
0.0013212551771
 
0.1%
0.0015645987971
 
0.1%
0.0016290414491
 
0.1%
0.0041112357641
 
0.1%
0.0042314110011
 
0.1%
ValueCountFrequency (%)
0.76992771312
 
0.2%
0.74796782481
 
0.1%
0.69885244241
 
0.1%
0.62201032682
 
0.2%
0.59185427181
 
0.1%
0.57935796721
 
0.1%
0.56944863921
 
0.1%
0.55598579757
0.7%
0.54302139411
 
0.1%
0.53307763382
 
0.2%

NoisePollutionRailwayL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct154
Distinct (%)30.4%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.01155228602
Minimum0
Maximum0.1282648481
Zeros321
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.424065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01076803394
95-th percentile0.05986415372
Maximum0.1282648481
Range0.1282648481
Interquartile range (IQR)0.01076803394

Descriptive statistics

Standard deviation0.02319201844
Coefficient of variation (CV)2.007569618
Kurtosis6.222613121
Mean0.01155228602
Median Absolute Deviation (MAD)0
Skewness2.470267201
Sum5.845456727
Variance0.0005378697192
MonotonicityNot monotonic
2023-01-16T16:50:29.514565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0321
32.1%
0.019328780815
 
0.5%
0.045626043544
 
0.4%
0.001153777223
 
0.3%
0.0398717993
 
0.3%
0.020772082073
 
0.3%
0.0049412786822
 
0.2%
0.044878671152
 
0.2%
0.056435796432
 
0.2%
0.014889752142
 
0.2%
Other values (144)159
 
15.9%
(Missing)494
49.4%
ValueCountFrequency (%)
0321
32.1%
3.268614761 × 10-51
 
0.1%
4.611411707 × 10-51
 
0.1%
8.449957979 × 10-51
 
0.1%
0.00015522950971
 
0.1%
0.00019924287711
 
0.1%
0.00031239459081
 
0.1%
0.00031757201352
 
0.2%
0.00068749712741
 
0.1%
0.00075798280631
 
0.1%
ValueCountFrequency (%)
0.12826484811
0.1%
0.11687070892
0.2%
0.10894600491
0.1%
0.1088058091
0.1%
0.10874602151
0.1%
0.1007803412
0.2%
0.10047784211
0.1%
0.099177404121
0.1%
0.094538612621
0.1%
0.092111552471
0.1%

NoisePollutionRailwayM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct117
Distinct (%)23.1%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.01085048205
Minimum0
Maximum0.1793079249
Zeros373
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.593065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.26512962 × 10-5
95-th percentile0.07467264392
Maximum0.1793079249
Range0.1793079249
Interquartile range (IQR)8.26512962 × 10-5

Descriptive statistics

Standard deviation0.02972206689
Coefficient of variation (CV)2.739239303
Kurtosis10.72970499
Mean0.01085048205
Median Absolute Deviation (MAD)0
Skewness3.291599476
Sum5.490343919
Variance0.0008834012605
MonotonicityNot monotonic
2023-01-16T16:50:29.668066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0373
37.3%
0.020602735374
 
0.4%
0.00054950157883
 
0.3%
0.035416532542
 
0.2%
0.0046198521652
 
0.2%
0.066639577412
 
0.2%
0.13482210322
 
0.2%
0.033873984262
 
0.2%
0.0092009916092
 
0.2%
0.0012213482462
 
0.2%
Other values (107)112
 
11.2%
(Missing)494
49.4%
ValueCountFrequency (%)
0373
37.3%
4.619364375 × 10-51
 
0.1%
6.1447708 × 10-51
 
0.1%
6.161049843 × 10-51
 
0.1%
7.728930934 × 10-51
 
0.1%
7.85274532 × 10-52
 
0.2%
8.402591054 × 10-51
 
0.1%
0.00010691593352
 
0.2%
0.00011935832961
 
0.1%
0.00019395481631
 
0.1%
ValueCountFrequency (%)
0.17930792491
0.1%
0.16540868271
0.1%
0.15568862281
0.1%
0.14532381471
0.1%
0.14315094211
0.1%
0.13980845311
0.1%
0.13876297581
0.1%
0.13482210322
0.2%
0.12955751311
0.1%
0.12885694251
0.1%

NoisePollutionRailwayS
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct62
Distinct (%)12.3%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.008839980359
Minimum0
Maximum0.2987745098
Zeros437
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.749565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.04748022101
Maximum0.2987745098
Range0.2987745098
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03638141229
Coefficient of variation (CV)4.115553521
Kurtosis29.56983729
Mean0.008839980359
Median Absolute Deviation (MAD)0
Skewness5.23863082
Sum4.473030062
Variance0.00132360716
MonotonicityNot monotonic
2023-01-16T16:50:29.820065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0437
43.7%
0.027371876322
 
0.2%
0.041327623132
 
0.2%
0.00060459492142
 
0.2%
0.012390670552
 
0.2%
0.0010183299392
 
0.2%
0.29877450982
 
0.2%
0.021535181242
 
0.2%
0.023952762922
 
0.2%
0.11940298511
 
0.1%
Other values (52)52
 
5.2%
(Missing)494
49.4%
ValueCountFrequency (%)
0437
43.7%
5.006007209 × 10-51
 
0.1%
0.00020064205461
 
0.1%
0.00020088388911
 
0.1%
0.00020161290321
 
0.1%
0.00030181086521
 
0.1%
0.00060459492142
 
0.2%
0.0007028112451
 
0.1%
0.0008606723371
 
0.1%
0.0010183299392
 
0.2%
ValueCountFrequency (%)
0.29877450982
0.2%
0.24567853461
0.1%
0.22320971871
0.1%
0.21126089321
0.1%
0.20101713061
0.1%
0.18928016571
0.1%
0.18202647661
0.1%
0.1796139361
0.1%
0.17243186581
0.1%
0.16824092251
0.1%

NoisePollutionRoadL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct411
Distinct (%)81.2%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.2256832477
Minimum0
Maximum0.6389892784
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:29.895565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04701504348
Q10.1320421044
median0.2109647519
Q30.3036385704
95-th percentile0.4709108442
Maximum0.6389892784
Range0.6389892784
Interquartile range (IQR)0.171596466

Descriptive statistics

Standard deviation0.1273428966
Coefficient of variation (CV)0.5642549809
Kurtosis0.03004224388
Mean0.2256832477
Median Absolute Deviation (MAD)0.08331380384
Skewness0.653235523
Sum114.1957233
Variance0.01621621332
MonotonicityNot monotonic
2023-01-16T16:50:30.046065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.047015043487
 
0.7%
0.50668199975
 
0.5%
0.14961474484
 
0.4%
0.22485145374
 
0.4%
0.061409747544
 
0.4%
0.13072401853
 
0.3%
0.27371367623
 
0.3%
0.32329756043
 
0.3%
0.16124540793
 
0.3%
0.63898927843
 
0.3%
Other values (401)467
46.7%
(Missing)494
49.4%
ValueCountFrequency (%)
01
0.1%
0.0069974034471
0.1%
0.0071359996251
0.1%
0.016112910571
0.1%
0.024010646671
0.1%
0.024656172331
0.1%
0.025510204081
0.1%
0.025655860031
0.1%
0.026671967821
0.1%
0.031060932392
0.2%
ValueCountFrequency (%)
0.63898927843
0.3%
0.54356691381
 
0.1%
0.53770294751
 
0.1%
0.53477885821
 
0.1%
0.53410283541
 
0.1%
0.51121673191
 
0.1%
0.51039181751
 
0.1%
0.50943139761
 
0.1%
0.50739833233
0.3%
0.50668199975
0.5%

NoisePollutionRoadM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct411
Distinct (%)81.2%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.2502618431
Minimum0
Maximum0.6164580887
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.122065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04905241535
Q10.1476138402
median0.2484047449
Q30.3433173736
95-th percentile0.4836870039
Maximum0.6164580887
Range0.6164580887
Interquartile range (IQR)0.1957035334

Descriptive statistics

Standard deviation0.1288296489
Coefficient of variation (CV)0.5147794299
Kurtosis-0.6397234614
Mean0.2502618431
Median Absolute Deviation (MAD)0.09687805643
Skewness0.1620821236
Sum126.6324926
Variance0.01659707844
MonotonicityNot monotonic
2023-01-16T16:50:30.195064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.049052415357
 
0.7%
0.48392296975
 
0.5%
0.23148810424
 
0.4%
0.32716266014
 
0.4%
0.065433971924
 
0.4%
0.22077280063
 
0.3%
0.4023971153
 
0.3%
0.31519971253
 
0.3%
0.24032819183
 
0.3%
0.4886069343
 
0.3%
Other values (401)467
46.7%
(Missing)494
49.4%
ValueCountFrequency (%)
01
0.1%
0.0069439989741
0.1%
0.0076390640772
0.2%
0.0079585280371
0.1%
0.011548380781
0.1%
0.014902237391
0.1%
0.016587601771
0.1%
0.018532736541
0.1%
0.021551724141
0.1%
0.023129056251
0.1%
ValueCountFrequency (%)
0.61645808871
0.1%
0.59611940871
0.1%
0.589001871
0.1%
0.52547008251
0.1%
0.5194516561
0.1%
0.5163813581
0.1%
0.51550460791
0.1%
0.51316604711
0.1%
0.50139894672
0.2%
0.49605517192
0.2%

NoisePollutionRoadS
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct407
Distinct (%)80.4%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.2784532628
Minimum0
Maximum0.6861177525
Zeros6
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.276066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04170959191
Q10.1573945616
median0.2817924959
Q30.3964871367
95-th percentile0.5150859135
Maximum0.6861177525
Range0.6861177525
Interquartile range (IQR)0.2390925751

Descriptive statistics

Standard deviation0.1521066892
Coefficient of variation (CV)0.5462557261
Kurtosis-0.5762569542
Mean0.2784532628
Median Absolute Deviation (MAD)0.1204538953
Skewness0.1908166936
Sum140.897351
Variance0.02313644491
MonotonicityNot monotonic
2023-01-16T16:50:30.349065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.097100782337
 
0.7%
06
 
0.6%
0.68611775255
 
0.5%
0.29439432994
 
0.4%
0.091063053674
 
0.4%
0.30906674544
 
0.4%
0.10634233323
 
0.3%
0.44337223223
 
0.3%
0.28074104233
 
0.3%
0.11521464653
 
0.3%
Other values (397)464
46.4%
(Missing)494
49.4%
ValueCountFrequency (%)
06
0.6%
0.00050709939151
 
0.1%
0.0015268729641
 
0.1%
0.0020920502091
 
0.1%
0.0022959183672
 
0.2%
0.0098308668081
 
0.1%
0.012366903281
 
0.1%
0.016355140191
 
0.1%
0.016807658061
 
0.1%
0.021229404311
 
0.1%
ValueCountFrequency (%)
0.68611775255
0.5%
0.67238058551
 
0.1%
0.64524694641
 
0.1%
0.61827531651
 
0.1%
0.60721457261
 
0.1%
0.59804485342
 
0.2%
0.59010475652
 
0.2%
0.58255298181
 
0.1%
0.57781995661
 
0.1%
0.54955858751
 
0.1%

Plot_area
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct109
Distinct (%)93.2%
Missing883
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean893.1196581
Minimum30
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.428565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile89.8
Q1342
median619
Q31000
95-th percentile1917.8
Maximum9000
Range8970
Interquartile range (IQR)658

Descriptive statistics

Standard deviation1203.536872
Coefficient of variation (CV)1.34756509
Kurtosis24.29570627
Mean893.1196581
Median Absolute Deviation (MAD)329
Skewness4.516865967
Sum104495
Variance1448501.003
MonotonicityNot monotonic
2023-01-16T16:50:30.502565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12002
 
0.2%
4172
 
0.2%
4002
 
0.2%
5002
 
0.2%
3602
 
0.2%
6992
 
0.2%
4032
 
0.2%
10002
 
0.2%
8401
 
0.1%
6561
 
0.1%
Other values (99)99
 
9.9%
(Missing)883
88.3%
ValueCountFrequency (%)
301
0.1%
521
0.1%
541
0.1%
601
0.1%
701
0.1%
851
0.1%
911
0.1%
931
0.1%
1001
0.1%
1241
0.1%
ValueCountFrequency (%)
90001
0.1%
67541
0.1%
61101
0.1%
35641
0.1%
30001
0.1%
22371
0.1%
18381
0.1%
18191
0.1%
18001
0.1%
17891
0.1%

PopulationDensityL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct411
Distinct (%)81.2%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.1606907277
Minimum0.002524030382
Maximum0.6875655101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.579065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.002524030382
5-th percentile0.01220132729
Q10.05528064102
median0.1263144656
Q30.2266858282
95-th percentile0.4453161186
Maximum0.6875655101
Range0.6850414797
Interquartile range (IQR)0.1714051872

Descriptive statistics

Standard deviation0.1371583185
Coefficient of variation (CV)0.8535546539
Kurtosis1.462343308
Mean0.1606907277
Median Absolute Deviation (MAD)0.07729745812
Skewness1.304317045
Sum81.30950824
Variance0.01881240434
MonotonicityNot monotonic
2023-01-16T16:50:30.664065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0097905571387
 
0.7%
0.59102989415
 
0.5%
0.029456623234
 
0.4%
0.31547872894
 
0.4%
0.055280641024
 
0.4%
0.11745746823
 
0.3%
0.36722707753
 
0.3%
0.24262815343
 
0.3%
0.14783244183
 
0.3%
0.39736800093
 
0.3%
Other values (401)467
46.7%
(Missing)494
49.4%
ValueCountFrequency (%)
0.0025240303821
0.1%
0.0025260739362
0.2%
0.0030815958862
0.2%
0.004806471591
0.1%
0.0050645892321
0.1%
0.0065547324071
0.1%
0.0069015113821
0.1%
0.0081042932961
0.1%
0.0091672879582
0.2%
0.0097776793441
0.1%
ValueCountFrequency (%)
0.68756551011
 
0.1%
0.63085622881
 
0.1%
0.6293898871
 
0.1%
0.59844194211
 
0.1%
0.59102989415
0.5%
0.57700038561
 
0.1%
0.56992651141
 
0.1%
0.56991148071
 
0.1%
0.56043692861
 
0.1%
0.53444377231
 
0.1%

PopulationDensityM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct411
Distinct (%)81.2%
Missing494
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.2298702705
Minimum0.001466111698
Maximum0.9239637627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.744065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001466111698
5-th percentile0.01875199438
Q10.1056895788
median0.1894008618
Q30.3294739982
95-th percentile0.5356565985
Maximum0.9239637627
Range0.922497651
Interquartile range (IQR)0.2237844194

Descriptive statistics

Standard deviation0.1622518477
Coefficient of variation (CV)0.7058409393
Kurtosis0.5054301827
Mean0.2298702705
Median Absolute Deviation (MAD)0.1027711536
Skewness0.8968429926
Sum116.3143569
Variance0.02632566208
MonotonicityNot monotonic
2023-01-16T16:50:30.817064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.016301763537
 
0.7%
0.47308602225
 
0.5%
0.068662979214
 
0.4%
0.45913647464
 
0.4%
0.1518528594
 
0.4%
0.13027327123
 
0.3%
0.53565659853
 
0.3%
0.27164718253
 
0.3%
0.23239600793
 
0.3%
0.56358405063
 
0.3%
Other values (401)467
46.7%
(Missing)494
49.4%
ValueCountFrequency (%)
0.0014661116982
0.2%
0.0047699991611
0.1%
0.00716517811
0.1%
0.0077253780742
0.2%
0.0079827955652
0.2%
0.0080891773261
0.1%
0.0092930474741
0.1%
0.0095696699561
0.1%
0.012663785311
0.1%
0.012934055261
0.1%
ValueCountFrequency (%)
0.92396376271
0.1%
0.77736422171
0.1%
0.73702757481
0.1%
0.71307856311
0.1%
0.7060686191
0.1%
0.69681120521
0.1%
0.69496853321
0.1%
0.62773015561
0.1%
0.60258171981
0.1%
0.59886905521
0.1%

living_area
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct196
Distinct (%)40.6%
Missing517
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean151.757764
Minimum25
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:30.968064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile53.1
Q195.5
median130
Q3181
95-th percentile319.7
Maximum700
Range675
Interquartile range (IQR)85.5

Descriptive statistics

Standard deviation89.96134092
Coefficient of variation (CV)0.5927956406
Kurtosis7.13860986
Mean151.757764
Median Absolute Deviation (MAD)40
Skewness2.172213113
Sum73299
Variance8093.04286
MonotonicityNot monotonic
2023-01-16T16:50:31.039064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16017
 
1.7%
13012
 
1.2%
12010
 
1.0%
1009
 
0.9%
3009
 
0.9%
908
 
0.8%
1408
 
0.8%
977
 
0.7%
1807
 
0.7%
1106
 
0.6%
Other values (186)390
39.0%
(Missing)517
51.7%
ValueCountFrequency (%)
251
 
0.1%
351
 
0.1%
371
 
0.1%
381
 
0.1%
391
 
0.1%
404
0.4%
431
 
0.1%
461
 
0.1%
482
0.2%
492
0.2%
ValueCountFrequency (%)
7001
0.1%
6001
0.1%
5831
0.1%
5601
0.1%
5501
0.1%
5091
0.1%
5001
0.1%
4501
0.1%
4301
0.1%
4251
0.1%

rooms_combined
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)5.1%
Missing508
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean4.960365854
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-16T16:50:31.106565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.5
median4.5
Q35.5
95-th percentile8
Maximum18
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.980802535
Coefficient of variation (CV)0.3993258953
Kurtosis6.361611852
Mean4.960365854
Median Absolute Deviation (MAD)1
Skewness1.560790132
Sum2440.5
Variance3.923578685
MonotonicityNot monotonic
2023-01-16T16:50:31.162568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.5127
 
12.7%
5.584
 
8.4%
3.571
 
7.1%
6.539
 
3.9%
2.538
 
3.8%
621
 
2.1%
7.514
 
1.4%
514
 
1.4%
713
 
1.3%
810
 
1.0%
Other values (15)61
 
6.1%
(Missing)508
50.8%
ValueCountFrequency (%)
15
 
0.5%
1.57
 
0.7%
28
 
0.8%
2.538
 
3.8%
310
 
1.0%
3.571
7.1%
48
 
0.8%
4.5127
12.7%
514
 
1.4%
5.584
8.4%
ValueCountFrequency (%)
181
 
0.1%
161
 
0.1%
131
 
0.1%
12.51
 
0.1%
115
0.5%
10.51
 
0.1%
103
0.3%
9.53
0.3%
93
0.3%
8.54
0.4%

Interactions

2023-01-16T16:50:26.593565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:07.016565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:08.119064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:09.220068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:11.468068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:12.805565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:14.011065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:15.195566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:16.332068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:17.525565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:18.682065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:19.828567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:20.966566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:22.142065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:23.151565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:24.319065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:25.436064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:26.651565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:07.081065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:08.178064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:09.319067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-16T16:50:12.870065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-01-16T16:50:27.405566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:07.878567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:08.877568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:10.971067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:12.456065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:13.670564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:14.854566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:16.052564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:17.262065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:18.427066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:19.575065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:20.710565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:21.882565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:22.931065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:24.059565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:25.114565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:26.259065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:27.471565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:07.941564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:08.941567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:11.105566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:12.606064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:13.739066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:14.927065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:16.134066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:17.330564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:18.493065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:19.640065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:20.775565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:21.950065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:22.984065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:24.125065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:25.177065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:26.324565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:27.532065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:07.998565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:09.023568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:11.227566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:12.667565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:13.878565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:14.989565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:16.199565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:17.392565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:18.553065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:19.700564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:20.835567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:22.011064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:23.041065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:24.186066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:25.309066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:26.386064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:27.597565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:08.059065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:09.120069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:11.343066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:12.733565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:13.944065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:15.130565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:16.265064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:17.459567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:18.617564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:19.764565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:20.901564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:22.076566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:23.093565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:24.253568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:25.375064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-16T16:50:26.453064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-16T16:50:31.231066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-16T16:50:31.373066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-16T16:50:31.518565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-16T16:50:31.666065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-16T16:50:27.785564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-16T16:50:27.955065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-16T16:50:28.123564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-16T16:50:28.328064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_index0FloorForestDensityLForestDensityMForestDensitySNoisePollutionRailwayLNoisePollutionRailwayMNoisePollutionRailwaySNoisePollutionRoadLNoisePollutionRoadMNoisePollutionRoadSPlot_areaPopulationDensityLPopulationDensityMliving_arearooms_combined
03564NaN1.00.2755120.1560810.2103520.0000000.0000000.00.1312700.1430570.123900NaN0.0197710.039614119.05.5
1801NaNNaN0.2232020.1289190.0101650.0000000.0000000.00.2839240.3543130.434566NaN0.0935780.185027116.04.5
219476NaN0.00.1107770.0407780.0000000.0000000.0000000.00.1701250.1713160.094135NaN0.0897550.196065130.04.5
323402991000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
415230NaNNaN0.2438510.0980600.0000000.0000000.0000000.00.1323480.2319780.366476NaN0.0210850.03781280.03.0
57959690000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6204682800000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72132NaNNaN0.4115500.3940170.1459430.0000000.0000000.00.0440760.0669940.089483NaN0.0065550.009570NaN4.5
83921790000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
915301NaNNaN0.0048760.0133870.0000000.0588490.0237030.00.3103680.3356880.189308NaN0.4938920.556291247.06.5

Last rows

df_index0FloorForestDensityLForestDensityMForestDensitySNoisePollutionRailwayLNoisePollutionRailwayMNoisePollutionRailwaySNoisePollutionRoadLNoisePollutionRoadMNoisePollutionRoadSPlot_areaPopulationDensityLPopulationDensityMliving_arearooms_combined
99017160490000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9917145790000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
992102671550000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99320917215000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
994200461390000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99516088NaN0.00.1753360.0772380.0000000.00.00.00.1213650.1596310.245363NaN0.0472520.09264989.03.5
9967722440000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9979383NaNNaN0.6201700.3640020.0009670.00.00.00.0614100.0654340.09106330.00.0552810.15185390.03.5
99820457443000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99912531NaN2.00.6008310.4969030.4898970.00.00.00.2598580.3839540.464767NaN0.0317980.092418163.05.5